This paper investigates the asymptotic distribution of the maximum-likelihood estimate (MLE) in multinomial logistic models in the high-dimensional regime where dimension and sample size are of the same order.

Monte Carlo stopping rules in a manner that is both sample efficient and robust to estimation error.

Modeling the human as Boltzmann-rational w.r.t. a belief over trajectories, we prove conditions under which RLHF is

The goal is to train a good instance classifier.

These results are then generalized to general models and likelihoods, where the same claims hold.